Accelerated query response
Improved team productivity
Automated data processing
Enhanced response accuracy
The challenge
Slow response to marketing intelligence queries
A leading manufacturer struggled with slow response times (up to two days) for marketing intelligence queries. Locating and analyzing scattered market trend and competitor data was cumbersome. The MI team spent half their time filtering and tagging, hindering efficiency and productivity.
Key challenges
Slow data retrieval
Manual data processing
Reduced productivity
The solution
GenAI-powered Knowledge Assist on Azure
Rapid data ingestion
GenAI ingested thousands of documents.
Processed market trend competitor data quickly
Fast access to relevant information
Intuitive query interface
ChatGPT-like interface for MI team
Retrieved information in seconds
Easy-to-use knowledge access
Contextual understanding
Fine-tuned for MI-related terminology
Seamlessly collated data from sources
Provided concise, relevant summaries
Automated data tasks
Automated filtering and tagging processes
Reduced manual effort significantly
Allowed focus on strategic work
Implementation approach
1
Azure-Based Solution
Deployed on the client's Azure environment
Ensured security and integration
Leveraged existing cloud infrastructure
2
GenAI Knowledge engine
Ingested vast amounts of MI data
Trained for contextual understanding
Optimized for rapid information retrieval
3
User-friendly interface
Mimicked familiar chat applications
Simplified query submission process
Enabled quick access to insights
4
Automated manual work
Implemented automated filtering of content
Automated tagging of relevant data
Freed up team for higher-value tasks
The impact
Enhanced productivity and faster, accurate responses
Better team productivity
MI team productivity increased by 40%
More time for strategic analysis
Higher output with same resources
Accelerated response times
Response times improved by 50%
Faster delivery of critical intelligence
Improved agility in decision-making
Automated data processing:
80% of filtering and tagging automated
Significant reduction in manual work
Increased operational efficiency
Improved response accuracy
85% accuracy rate in responses
More reliable marketing intelligence
Enhanced trust in provided insights
Looking ahead
Further automation
More opportunities for automated MI tasks
Enhanced accuracy
Continuously refine GenAI models for even higher precision
Broader data integration
Integrate more diverse data sources for richer insights
